13 May Types of Regression in Machine Learning

Regression is a supervised learning technique in machine learning used for predicting continuous numerical values based on input features. There are several types of regression, each suited for different kinds of data and problem scenarios.

Types of Regression in Machine Learning

1. Linear Regression
   • Models the relationship between a dependent variable (target) and one or more independent variables (features) using a linear equation.
   • Assumes a linear relationship between inputs and output.
   • Types:
     • Simple Linear Regression (1 feature)
     • Multiple Linear Regression (multiple features)
2. Polynomial Regression
   • Extends linear regression by adding polynomial terms (e.g., x2,x3x2,x3) to model nonlinear relationships.
   • Useful when data shows a curved trend.
3. Ridge Regression (L2 Regularization)
   • A variant of linear regression that adds an L2 penalty term to prevent overfitting by shrinking coefficients.
   • Helps when multicollinearity (high correlation among features) exists.
4. Lasso Regression (L1 Regularization)
   • Similar to Ridge but uses an L1 penalty, which can shrink some coefficients to zero, performing feature selection.
5. Elastic Net Regression
   • Combines L1 and L2 penalties (Ridge + Lasso) for better regularization when dealing with highly correlated features.
6. Logistic Regression
   • Despite its name, it is used for classification (binary/multiclass) rather than regression.
   • Models probabilities using a logistic function (sigmoid).
7. Support Vector Regression (SVR)
   • Uses Support Vector Machines (SVM) principles to perform regression by finding the best-fit hyperplane within a tolerance margin (epsilon tube).
8. Decision Tree Regression
   • Uses a decision tree structure to predict continuous values by splitting data into subsets based on feature thresholds.
9. Random Forest Regression
   • An ensemble method that combines multiple decision trees to improve prediction accuracy and reduce overfitting.
10. Gradient Boosting Regression (e.g., XGBoost, LightGBM, CatBoost)
    • Builds regression models sequentially, where each new model corrects errors from the previous one.
11. Bayesian Regression
    • Uses Bayesian inference to model uncertainty in regression coefficients.
12. Quantile Regression
    • Predicts different quantiles (e.g., median, 90th percentile) instead of just the mean.
13. Poisson Regression
    • Used for count data (non-negative integers) where the dependent variable follows a Poisson distribution.
14. Partial Least Squares (PLS) Regression
    • Used when features are highly collinear or when dimensionality reduction is needed.

Are Regression Types the Same as Algorithms

• Regression types define the mathematical approach (e.g., linear, polynomial, regularized).
• Algorithms are the computational methods used to fit these models (e.g., Ordinary Least Squares for Linear Regression, Gradient Descent for optimization).

For example:

• Linear Regression (type) can be implemented using Ordinary Least Squares (OLS) or Gradient Descent (algorithms).
• Decision Tree Regression (type) uses a tree-based splitting algorithm (like CART).

Here is the summary:

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